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Using machine learning to detect misstatements

Review of accounting studies, 2021-06, Vol.26 (2), p.468-519 [Peer Reviewed Journal]

Springer Science+Business Media, LLC, part of Springer Nature 2020 ;Springer Science+Business Media, LLC, part of Springer Nature 2020. ;ISSN: 1380-6653 ;EISSN: 1573-7136 ;DOI: 10.1007/s11142-020-09563-8

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  • Title:
    Using machine learning to detect misstatements
  • Author: Bertomeu, Jeremy ; Cheynel, Edwige ; Floyd, Eric ; Pan, Wenqiang
  • Subjects: Accounting irregularities ; Accounting/Auditing ; Artificial intelligence ; Business and Management ; Corporate Finance ; Machine learning ; Public Finance
  • Is Part Of: Review of accounting studies, 2021-06, Vol.26 (2), p.468-519
  • Description: Machine learning offers empirical methods to sift through accounting datasets with a large number of variables and limited a priori knowledge about functional forms. In this study, we show that these methods help detect and interpret patterns present in ongoing accounting misstatements. We use a wide set of variables from accounting, capital markets, governance, and auditing datasets to detect material misstatements. A primary insight of our analysis is that accounting variables, while they do not detect misstatements well on their own, become important with suitable interactions with audit and market variables. We also analyze differences between misstatements and irregularities, compare algorithms, examine one-year- and two-year-ahead predictions and interpret groups at greater risk of misstatements.
  • Publisher: New York: Springer US
  • Language: English
  • Identifier: ISSN: 1380-6653
    EISSN: 1573-7136
    DOI: 10.1007/s11142-020-09563-8
  • Source: ProQuest Central
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